Figure 4a and Supplementary Figure 5

Introduction

Methods

Results

url <- fileMap(type = "rds")["Figure 4b"][[1]]
data <- read_rds(gzcon(url(file.path(dataUrl, url))))

#TP53 status independant
#Set up all combinations of variables to calcualte survival probability for
independant <- expand.grid(
  gene = c("lncTAM34a", "miR34a", "TP53"),
  cancer = sort(unique(data$Cancer))
) %>% 
  as_tibble() %>%
  mutate_if(is.factor, as.character) %>%
  group_by(gene, cancer) %>%
  #add data of the corresponding cancer and, for gene expression, filter WT TP53
  mutate(data = map2(cancer, gene, function(c, g, cm = data) {
    if(g == "TP53") {
      filter(cm, Cancer == c)
    } else {
      filter(cm, Cancer == c & TP53 == FALSE)
    }
  })) %>%
  #select the gene to be examined
  mutate(data = pmap(list(gene, data), function(g, d) {
      select(d, g, vitalStatus, FU)
  })) %>%
  #remove samples where gene expression/mutation status is na
  mutate(data = map(data, function(x) filter(x, is.na(x[[1]]) == FALSE))) %>%
  #remove cancers where comparisons without data exist
  mutate(bool = map_lgl(data, function(x) nrow(x) == 0)) %>%
  group_by(cancer) %>%
  filter(!any(bool)) %>%
  #calculate the percentile for the gene's expression for each sample
  mutate(data = map2(gene, data, function(g, d) {
    if(any(grepl("TP53", colnames(d)))) {
      mutate(d, percentile = NA)
    } else {
      mutate(d, percentile = ntile(x = d[[1]], n = 10))
    }
  })) %>%
  #classify 10th percentile and under as "low" and the rest as "others"
  #classify TP53 mutated as low and WT as others
  mutate(data = map(data, function(d) {
    if(any(grepl("TP53", colnames(d)))) {
      mutate(d, class = if_else(TP53, "low", "others"))
    } else {
     mutate(d, class = if_else(percentile == 1, "low", "others")) 
    }
  })) %>%
  #calculate survival probability
  mutate(surv.obj = map(data, ~get_survival(.x))) %>%
  mutate(surv.fit = map2(data, surv.obj, function(x, y) survival_fit(y, x$class))) %>%
  mutate(p.value = map2_dbl(data, surv.obj, function(x, y) survival_p(y, x$class))) %>%
  ungroup()

trash <- pmap(
  list(independant$data, independant$surv.fit, independant$gene, independant$cancer, independant$p.value), 
  function(v, w, x, y, z) {
    plotKM(v, w, x, y, z)
  }
)